Analyzer for behavioral analysis and parameterization of neural stimulation

ABSTRACT

Embodiments are directed to a computer implemented neural stimulation system having a first module configured to derive neural data from muscle contractions or movements of a subject. The system further includes a second module configured to derive a neural state assessment of the subject based at least in part on the neural data. The system further includes a third module configured to derive at least one neural stimulation parameter based at least in part on the neural state assessment. The system further includes a fourth module configured to deliver neural stimulations to the subject based at least in part on the at least one neural stimulation parameter.

BACKGROUND

The present disclosure relates in general to systems and methodologiesfor controlling the therapeutic application of neural stimulation. Morespecifically, the present disclosure relates to systems andmethodologies for analyzing the results of muscle contractions ormovements in order to develop parameters for controlling the therapeuticapplication of neural stimulation to treat neurological disorders.

Different types of therapeutic stimulation may be applied toneurological systems to treat variety of neurological disorders. Forexample, therapeutic neural stimulation may be applied to the brain totreat a variety of brain related disorders. One example of a therapeuticneural stimulation system is known generally as deep brain stimulation(DBS). DBS is a neurosurgical procedure involving the implantation of amedical device called a brain pacemaker, which sends electricalimpulses, through implanted electrodes, to specific parts of the brain(brain nucleus) for the treatment of movement and affective disorders.DBS in select brain regions has provided therapeutic benefits forotherwise-treatment-resistant movement and affective disorders such asParkinson's disease, essential tremor, dystonia, chronic pain, majordepression and obsessive-compulsive disorder (OCD).

A contemporary DBS system includes three components, namely an implantedpulse generator (IPG), a lead and an extension. The IPG is abattery-powered neurostimulator encased in a titanium housing. The IPGsends electrical pulses to the brain to interfere with neural activityat the target site. The lead is a coiled wire insulated in polyurethanewith four platinum iridium electrodes and is placed in one or twodifferent nuclei of the brain. The lead is connected to the IPG by theextension, which is an insulated wire that runs below the skin andextends from the head down the side of the neck and behind the ear tothe IPG. The IPG is placed subcutaneously below the clavicle or, in somecases, the abdomen. DBS leads are placed in the brain according to thetype of symptoms to be addressed. For example, to treat non-Parkinsonianessential tremor, the lead is typically placed in the ventrointermediatenucleus of the thalamus.

Instruments for direct electrical brain stimulation are currentlyavailable from several companies (e.g., Medtronics, Neuromed, CochlearCorp., Advanced Bionics). An important step in applying neuralstimulation is determining the appropriate neural stimulation parametersin order to optimize efficacy of stimuli to be ultimately used intreatment. If the stimulation is electrical signals, the stimulationparameters may be frequency, pulse duration, duty cycle, etcetera. Theeffectiveness of a neural stimulation treatment can depend on theeffectiveness of the criteria for choosing the stimulation parameters ofa neural stimulation procedure.

SUMMARY

Embodiments are directed to a computer implemented neural stimulationsystem having a first module configured to derive neural data frommuscle contractions or movements of a subject. The system furtherincludes a second module configured to derive a neural state assessmentof the subject based at least in part on the neural data. The systemfurther includes a third module configured to derive at least one neuralstimulation parameter based at least in part on the neural stateassessment. The system further includes a fourth module configured todeliver neural stimulations to the subject based at least in part on theat least one neural stimulation parameter.

Embodiments are directed to a computer implemented neural stimulationmethod. The method includes deriving, by a processor, neural data frommuscle contractions or movements of a subject. The method furtherincludes deriving, by the processor, a neural state assessment of thesubject based at least in part on the neural data. The method furtherincludes deriving, by the processor, at least one neural stimulationparameter based at least in part on the neural state assessment. Themethod further includes delivering neural stimulations to the subjectbased at least in part on the at least one neural stimulation parameter.

Embodiments are further directed to a computer program product forimplementing a neural stimulation system. The computer program productincludes a computer readable storage medium having program instructionsembodied therewith, wherein the computer readable storage medium is nota transitory signal per se. The program instructions are readable by aprocessor circuit to cause the processor circuit to perform a methodthat includes deriving, by the processor, neural data from musclecontractions or movements of a subject. The method further includesderiving, by the processor, a neural state assessment of the subjectbased at least in part on the neural data. The method further includesderiving, by the processor, at least one neural stimulation parameterbased at least in part on said neural state assessment. The methodfurther includes delivering neural stimulations to the subject based atleast in part on the at least one neural stimulation parameter.

Additional features and advantages are realized through techniquesdescribed herein. Other embodiments and aspects are described in detailherein. For a better understanding, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as embodiments is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing node according to one or moreembodiments;

FIG. 2 depicts a cloud computing environment according to one or moreembodiments;

FIG. 3 depicts abstraction model layers according to one or moreembodiments;

FIG. 4 depicts a diagram illustrating a neural stimulation systemaccording to one or more embodiments;

FIG. 5 depicts a graphical text analyzer's output feature vectorcomprising an ordered set of words or phrases, wherein each isrepresented by its own vector according to one or more embodiments;

FIG. 6 depicts various equations illustrating a core algorithm of agraphical text analyzer in accordance with one or more embodiments;

FIG. 7 depicts of a diagram of a graphical text analysis and brainstimulation system according to one or more embodiments;

FIG. 8 depicts a flow diagram of a methodology according to one or moreembodiments; and

FIG. 9 depicts a diagram of a computer program product according to oneor more embodiments.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with three digit reference numbers. The leftmost digits ofeach reference number corresponds to the figure in which its element isfirst illustrated.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed. Additionally, although this disclosure includes adetailed description of analyzing text in order to derive parameters ofa deep brain stimulation, implementation of the teachings recited hereinare not limited to graphical text systems and deep brain stimulationsystems. Rather, embodiments of the present disclosure are capable ofbeing implemented in conjunction with any other type of system, nowknown or later developed, that analyzes any type of voluntary orinvoluntary muscle contractions or movements. Embodiments of the presentdisclosure are further capable of being implemented in conjunction withany other type of system, now known or later developed, that appliestherapeutic neural stimulation to neurological systems.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows: Software as a Service (SaaS): thecapability provided to the consumer is to use the provider'sapplications running on a cloud infrastructure. The applications areaccessible from various client devices through a thin client interfacesuch as a web browser (e.g., web-based e-mail). The consumer does notmanage or control the underlying cloud infrastructure including network,servers, operating systems, storage, or even individual applicationcapabilities, with the possible exception of limited user-specificapplication configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a neural stimulation system supportmodule 96 for developing parameters to control the therapeuticapplication of neural stimulation to treat neurological disorders.

The present disclosure relates in general to systems and methodologiesfor controlling the therapeutic application of neural stimulation. Morespecifically, the present disclosure relates to systems andmethodologies for analyzing the results of muscle contractions ormovements in order to develop parameters for controlling the therapeuticapplication of neural stimulation to treat neurological disorders.Workloads layer and neural stimulation system support module 96 mayprovide some or all of the functionality to support the one or moreembodiments of the present disclosure.

As previously noted herein, different types of therapeutic stimulationmay be applied to neurological systems to treat variety of neurologicaldisorders. For example, therapeutic neural stimulation may be applied tothe brain to treat a variety of brain related disorders. One example ofa therapeutic neural stimulation system is known generally as DBS. DBSis a neurosurgical procedure involving the implantation of a medicaldevice called a brain pacemaker, which sends electrical impulses,through implanted electrodes, to specific parts of the brain (brainnucleus) for the treatment of movement and affective disorders. DBS inselect brain regions has provided therapeutic benefits forotherwise-treatment-resistant movement and affective disorders such asParkinson's disease, essential tremor, dystonia, chronic pain, majordepression and OCD.

An important step in applying neural stimulation is determining theappropriate neural stimulation parameters in order to optimize efficacyof stimuli to be ultimately used in treatment. If the stimulation iselectrical signals, the stimulation parameters may be frequency, pulseduration, duty cycle, etcetera. The effectiveness of a neuralstimulation treatment can depend on the effectiveness of the criteriafor choosing the stimulation parameters of a neural stimulationprocedure. Accordingly, achieving a natural classification of a giventrajectory/graph of behavior and cognitive function is an optimalrequirement for setting or modifying parameters and confidence levelsfor stimulation via neural stimulators such as DBS. Such aclassification is often not available, or takes too long to determinefor real-time intervention in neural processes such as brain activity.

The present disclosure provides a method for continuous graphical textanalyzer calculations on an individual's speech, wherein suchcalculations produce categories and confidence levels which are input toa stimulus parameter set translation for neural stimulation (e.g., DBS).When a subject is near a transition into a state of psychiatricdysfunction (e.g., signs of impending delusions or schizophrenicthinking), the system of the present disclosure triggers neuralstimulation having stimulation parameters selected to treat the state ofpsychiatric dysfunction. For example, where the neural stimulation isDBS, the system of the present disclosure triggers DBS in a particularset of output electrodes implanted in the brain. The graphical textanalyzer utilized in connection with the present disclosure quantifies atrajectory or realization of a stochastic process, i.e., a sequence ofstates (e.g., multi-dimensional vectors) such as words in text.

In an exemplary embodiment according to the present disclosure, anindividual undergoes DBS, and a system analyzes text-to-speech of theindividual as a set of word or phrase vectors to categorize theindividual's cognitive state. A preliminary category of the cognitivestate is determined and used to predict the individual's cognitivetrajectory and to update the individual's DBS. Additional data samplesare extracted. The additional data samples may include written text,electroencephalogram (EEG) measurements, deep brain electrophysiology,behaviors in the world, etcetera, derived from a cloud computingenvironment, wearable devices on the individual, implanted DBSelectrodes, and others. The additional data provide additional cognitivetrajectories within a graph. The probability that a particular Markovchain produced a given trajectory is calculated. A Markov chain is amathematical system that undergoes transitions from one state to anotheron a state space. It is a random process usually characterized asmemoryless, i.e., the next state depends only on the current state andnot on the sequence of events that preceded it. This specific kind of“memorylessness” is called the Markov property. Markov chains have manyapplications as statistical models of real-world processes. The DBSparameters used as one of the additional data samples, along with asecond classification of the cognitive state, are used to determine if achange in the individual's cognitive state was clinically beneficial.The stimulation parameters are selected based at least in part on anidentification of the simulations parameters that achieved behaviorreclassifications/transition to another chain category and clinicaloutcome.

Turning now to a more detailed description of the present disclosure,FIG. 4 depicts a diagram illustrating a neural stimulation system 400interacting with a human 402 according to one or more embodiments.Neural stimulation system 400 includes an analyzer module 406, a musclecontractions or movements module 408, an other inputs module 410, cloud50 (also shown in FIG. 2), a neural state assessment module 412, aneural stimulation parameters module 414 and a neural stimulator module416, configured and arranged as shown. Cloud 50 may supplement, supportor replace some or all of the functionality of muscle contractions ormovements module 408, other inputs module 410, analyzer module 406,neural state assessment module 412, neural stimulation parameters module414 and neural stimulator module 416. Additionally, some or all of thefunctionality of muscle contractions or movements module 408, otherinputs module 410, analyzer module 406, neural state assessment module412, neural stimulation parameters module 414 and neural stimulatormodule 416 may be implemented as a node 10 (shown in FIGS. 1 and 2) ofcloud 50.

Neural stimulation system 400 determines the appropriate neuralstimulation parameters (module 412) for applying neural stimulation to aneural system (e.g., brain 404) of human 402. Although brain 404 isshown in FIG. 4, it is understood that the present disclosure isapplicable to stimulation applied to portions of the neural system ofhuman 402 that extend beyond brain 404. Referring again to neuralstimulation system 400, modules 408, 410 may work together orindividually to provide inputs to analyzer module 406. Morespecifically, muscle contractions or movements module 408 senses theresults of both voluntary and involuntary muscle contractions ormovements by human 402, and other input module 410 optionally convertsdetected muscle contractions or movements into alternative formats. Forexample, where muscle contractions or movements detected by module 408are the muscle contractions or movements that generate speech, otherinputs module 410 may convert the detected speech to text. Othergestures created by muscle contractions or movements may also be reducedto tokens such as text descriptors, or nodes in a graph. In one or moreembodiments, modules 408, 410 may also work together or individually togenerate and/or detect voice, video or any other measurement that can beinformative about a current neurological state (e.g., mental/emotionalstates) and changes to that neurological state (e.g., heart ratevariability, blood pressure, galvanic skin conductance and EEGmeasurements, each of which may be collected using easy-to-use,inexpensive wearable devices). Module 408 may be implemented as anydevice that can detect and/or measure voluntary and/or involuntarymuscle contractions or movements. Module 410 may be implemented as anydevice that can convert detected and/or measured voluntary and/orinvoluntary muscle contractions or movements to another format, such asa conversion of speech to text.

Analyzer module 406 extracts features of the various text, voice, videoand other physiological measurements it receives. Each type ofmeasurements may be used to generate a distinct set of features (e.g.,voice pitch, facial expression features, heart rate variability as anindicator of stress level, etc.). In addition, features may be extractedfrom a derived data set of predictions about neural systems, e.g., theirmorphology and neurophysiological properties, including neurodynamicalproperties and neural connectivity. This derived data, inferred frommeasures of behavior and their related features, is herein referred toas neural data. The extracted features are fed to neural stateassessment module 412 to determine a current or expected neural state(e.g., a predicted Parkinson's or OCD episode) of human 402 thatcorresponds to the extracted features. Neural stimulation parametersmodule 414 uses the neural state assessment made by module 412, as wellas other feedback inputs described in more detail below, to determinethe appropriate stimulation parameters to address the assessed neuralstate. For example, where the neural stimulation is electrical signals,the stimulation parameters may be frequency, pulse duration, duty cycle,etcetera. Module 414 then provides the stimulation parameters to neuralstimulator module 416, which in turn provides neural stimulationsaccording to the selected stimulation parameters to brain 404. Neuralstimulator module 416 may be implemented as a DBS stimulator. Neuralactivity is recorded in order to allow a subsequent assessment of theimpact of neural stimulations applied by neural stimulator module 416.Recorded neural activity is fed back to analyzer module 406 and neuralstimulator module 414 to further inform the subsequent featuresextractions made by analyzer module 406 and the subsequent selection ofneural stimulation parameters made by module 414.

Thus, neural stimulation system 400, and specifically the combinedeffects of analyzer module 406, muscle contractions or movements module408, other inputs module 410 and neural state assessment module 412,significantly expand the scope of data that may be used to both assesscurrent and future neural states, and to inform the selection of neuralstimulation parameters made by module 414. Significantly, the additionaldata accessed by neural stimulation system 400 are derived from musclecontractions or movements, which are non-invasive and generally saferand less costly to gather than direct neural readings made by, forexample, electrodes inserted within brain 404. Neural stimulation system400 additionally provides access to an even wider range of historicaldata of human 402 that may be provided to analyzer module 406 forfeature extraction analysis. For example, cloud 50 may provideinformation on from social graphs, emails, recorded interviews andconversations of human 402.

A more specific implementation of neural stimulation system 400 will nowbe described with reference to FIGS. 5-8, wherein the detected musclecontractions or movements of human 402 (shown in FIG. 4) are speech, thedetected speech is converted to text, the feature extraction isperformed by a graphical text analyzer, and the neural stimulator is aDBS stimulator. Referring now to FIG. 5, there is depicted a graphicaltext analyzer's output feature vector in the form of a word graph 500having an ordered set of words or phrases shown as nodes 502, 504, 506,each represented by its own features vector 510, 512, 514 according toone or more embodiments. Each features vector 510, 512, 514 isrepresentative of some additional feature of its corresponding node 502,504, 506 in some word/feature space. Word graph 500 is useful to extracttopological features for certain vectors, for example, all vectors thatpoint in the upper quadrant of the feature space of words. Thedimensions of the word/feature space might be parts of speech (verbs,nouns, adjectives), or the dimensions may be locations in a lexicon oran online resource of the semantic categorization of words in a featurespace such as WordNet, which is the trade name of a large lexicaldatabase of English. In WordNet, nouns, verbs, adjectives and adverbsare grouped into sets of cognitive synonyms (synsets), each expressing adistinct concept. Synsets are interlinked by means ofconceptual-semantic and lexical relations. The resulting network ofmeaningfully related words and concepts can be navigated with a browser.WordNet is also freely and publicly available for download from theWorldNet website, www.worldnet.princeton.edu. The structure of WordNetmakes it a useful tool for computational linguistics and naturallanguage processing.

FIG. 6 depicts Equations A-H, which illustrate features of a corealgorithm that may be implemented by an analyzer module 406A (shown inFIG. 7) having a graphical text analysis module 702 (shown in FIG. 7)according to one or more embodiments. Analyzer module 406A is animplementation of analyzer module 406 (shown in FIG. 4), wherein musclecontractions or movements of human 402 in the form of speech is detectedby module 408 (shown in FIG. 4) and converted to text by other inputsmodule 410.

Continuing with a description of Equations A-H of FIG. 6 includingselected references to corresponding elements of analyzer module 406Aand graphical text analysis module 702 shown in FIG. 7, text orspeech-to-text is fed into a standard lexical parser (e.g., syntacticfeature extractor 704 of FIG. 7) that extracts syntactic features, whichare converted to vectors. Such vectors can have binary components forthe syntactic categories verb, noun, pronoun, etcetera, such that thevector represented by Equation A represents a noun word.

The text is also fed into a semantic analyzer (e.g., semantic featureextractor 706 of FIG. 7) that converts words into semantic vectors. Theconversion into semantic vectors can be implemented in a number of ways,including, for example, the use of latent semantic analysis. Thesemantic content of each word is represented by a vector whosecomponents are determined by the singular value decomposition of wordco-occurrence frequencies over a large database of documents. As aresult, the semantic similarity between two words “a” and “b” can beestimated by the scalar product of their respective semantic vectorsrepresented by Equation B.

A hybrid graph is created in accordance with Equation C in which thenodes “N” represent words or phrases, the edges “E” represent temporalprecedence in the speech, and each node possesses a feature vector “W”defined as a direct sum of the syntactic and semantic vectors plusadditional non-textual features (e.g. the identity of the speaker) asgiven by Equation D.

The graph “G” of Equation C is then analyzed based on a variety offeatures, including standard graph-theoretical topological measures ofthe graph skeleton as shown by Equation E, such as degree distribution,density of small-size motifs, clustering, centrality, etcetera.Similarly, additional values can be extracted by including the featurevectors attached to each node. One such instance is the magnetization ofthe generalized Potts model as shown by Equation F such that temporalproximity and feature similarity are taken into account.

The features that incorporate the syntactic, semantic and dynamicalcomponents of speech are then combined as a multi-dimensional featuresvector “F” that represents the speech sample. This feature vector isfinally used to train a standard classifier according to Equation G todiscriminate speech samples that belong to different conditions “C,”such that for each test speech sample the classifier estimates itscondition identity based on the extracted features represented byEquation H.

FIG. 7 depicts a diagram of an analyzer module 406A having a graphicaltext analysis module 702 according to one or more embodiments. Analyzermodule 406A is an implementation of analyzer module 406 (shown in FIG.4), and text input 410A is an implementation of other inputs module 410(shown in FIG. 4). Analyzer module 406A includes text input 410A, asyntactic feature extractor 704, a semantic feature extractor 706, agraph constructor 708, a graph feature extractor 710, a hybrid graphmodule 712, a learning engine 714, a predictive engine 716 and an outputmodule 718, configured and arranged as shown. In general, graphical textanalyzer 702 functions to convert inputs from text input module 410Ainto hybrid graphs (e.g., word graph 500 shown in FIG. 5), which isprovided to learning engine 714 and predictive engine 716. In additionto the graphical text analyzer algorithm illustrated in FIG. 6 anddescribed above, additional details of the operation of graphical textanalyzer 702 are available in a publication entitled Speech GraphsProvide A Quantitative Measure Of Thought Disorder In Psychosis,authored by Mota et al., and published by PLOS ONE, April 2012, Volume7, Issue 4, the entire disclosure of which is incorporated by referenceherein in its entirety.

As noted, graphical text analyzer 702 provides word graph inputs tolearning engine 714, and predictive engine 716, which constructspredictive features or model classifiers of the state of the individualin order to predict what the next state will be, i.e., the predictedbehavioral or psychological category of output module 718. Accordingly,predictive engine 716 and output module 718 may be modeled as Markovchains. The Markovian model output from output module 718 may be fedinto neural stimulation parameter module 414 (shown in FIG. 4) toparameterize neural stimulation module 416 (shown in FIG. 4), which maybe implemented as a DBS. Accordingly, when a particular undesiredpsychological state is predicted by analyzer 406A, the undesiredpsychological state may be averted through the parameterization ofneural stimulation module 416. The actual neural activity may berecorded (e.g., recorded neural actively shown in FIG. 4) and fed backinto predictive engine 716 to predict the next state that will be givento neural stimulation parameters module 414 to parameterize neuralstimulator module 416.

FIG. 8 depicts a flow diagram of a methodology 800 performed by analyzer406A (shown in FIG. 7) according to one or more embodiments. Methodology800 begins at block 802 by developing graphs of text that has beenconverted from speech. At block 804, properties/features of graphs areanalyzed to classify or assign human 402 (shown in FIG. 4) in aparticular time window of speech to a particular brain state. Theclassification or assignment may be based on a scale, such as thePositive and Negative Syndrome Scale (PANSS), which is a medical scaleused for measuring symptom severity of patients with schizophrenia. Notethat PANSS scale ratings are performed according to a known specificprotocol. However, the classification or assignment made at 804 is aprediction of a PANSS scale rating, based on a configuration andtraining of the system to make this prediction, for example, by havingtrained the system on labelled data from patients. With human 402classified into a particular brain state (e.g., a predicted PANSS scalerating), block 806 analyzes additional data samples to extractadditional cognitive trajectories within the graph. The additional datasamples may include written text, EEG measurements, deep brainelectrophysiology, behaviors in the world, etcetera, derived from cloud50 (shown in FIGS. 3 and 4), wearable devices (not shown) on human 402,implanted DBS electrodes, and others.

At block 808, an underlying stochastic process (e.g., a Markov chain) isanalyzed, wherein transitions from state to state are probabilistic. Inother words, the transitions from state to state are governed byprobability such that the behavior of the probabilistic system cannot bepredicted exactly but the probability of certain behaviors is known. Atblock 810, the probability that a given trajectory was produced by aparticular Markov chain is determined. Continuing with the examplewherein the determination of the brain state is based on a predictedPANSS scale, by observing predicted PANSS scales over time as well asthe correlated speech features, a Markov-based model of cognitive statesand how those states are changing may be derived. For example, human 402may have two ways of having a schizophrenic episode. The predicted PANSSscale might change very rapidly, or it might change very gradually.Thus, the disclosed Markov model conveys whether or not human 402 is ina brain state that is about to change rapidly or gradually, which allowsmethodology 800 at block 812 to modify DBS parameters to change theclassification of ongoing behavior. At block 814 a Markov chain model iscreated including stimulation parameters that achieved behaviorreclassification/transition to another chain category. At block 816,given the Markov chain model subsequently observed, the same stimulationparameters are selected to achieve a similar reclassification.

Thus, block 810 allows block 812 to make a prediction about the DBSparameters that may be required to avert the brain state changeidentified by the predicted PANSS scale. The selected DBS parametersdepend on whether the Markov state is changing rapidly or gradually. Theselected parameters may then be validated by ongoing recordings ofspeech from human 402 with subsequent iterations of methodology 800mapping the features of human 402's speech and other additional databack to the predicted PANSS scale in order to determine whether or notthe Markov model needs to be updated, and also to determine whether ornot whether the selected DBS parameter change has been effective. NewMarkov states are continuously constructed until, over time, Markovstates have been constructed for every state that may be a target forstimulation. This creates a Markov scale that can be used to identifymultiple additional states. For example, it may be identified that apredicted PANSS scale is about to change rapidly, that a predicted PANSSscale is about to change gradually, that a predicted PANSS scale isabout to change rapidly and a DBS stimulation has been provided withcertain parameters, or that a predicted PANSS scale is about to changegradually and a DBS stimulation has been provided with certain otherparameters. This allows DBS stimulation parameters to be selected inreal time, and to be informed by information that is broader in scopeand safer and less costly to acquire than just the recorded brainactivity (as shown in FIG. 4) acting alone.

Thus it can be seen from the forgoing detailed description that one ormore embodiments of the present disclosure provide technical benefitsand advantages. The disclosed neural stimulation system non-invasivelyanalyzes data derived from either voluntary or involuntary musclecontractions or movements (e.g., speech, EEG measurements, pulse, heartrate, text converted from speech, video etc.) to construct features,then correlate those features with neural states to parameterize aneural stimulation (e.g., a DBS). The detected indicators of aparticular cognitive state or a disease state can be prescriptive of thetherapeutic parameter of the neural stimulation, as well as provide anindication of whether or not the applied neural stimulation has movedthe neural state toward restoration of a normal, health state.

More specifically, the disclosed neural stimulation system combinesmachine learning, graph theoretic and natural language techniques toimplement real-time analysis of behavior, including speech, to providequantifiable features extracted from in-person interviews,teleconferencing or offline sources (email, phone) for categorization ofpsychological states. The disclosed system collects both real time andoffline behavioral streams such as speech-to-text and text, video andphysiological measures such as heart rate, blood pressure and galvanicskin conductance. In some embodiments, speech and text data are analyzedonline for a multiplicity of features, such as semantic content andsyntactic structure in the transcribed text, emotional value from theaudio, and video and physiological streams. The analyzed speech and textare then combined with offline analysis of similar streams produced bythe patient and by the population. The disclosed methods of extractingsemantic vectors (e.g., latent semantic analysis and WordNet) allow thecomputation of a distance between words and specific concepts (e.g.,introspection, anxiety, depression), such that the text can betransformed into a field of distances to a concept, a field of fields ofdistances to the entire lexicon, or a field of distances to other textsincluding books, essays, chapters and textbooks.

The syntactic and semantic features are combined either as an assemblageof features or as integrated fields, such as the Potts model. Similarly,locally embedded graphs may be constructed such that a trajectory in ahigh-dimensional feature space is computed for each text. The trajectoryis used as a measure of coherence of the speech, as well as a measure ofdistance between speech trajectories using methods such as dynamic timewarping. The extracted multi-dimensional features are then used aspredictors for psychological states (such as communication and cognitivestyle, or psychiatric disorders) based on training with pre-labeledinterviews with test individuals. This method produces models thattransform text into a field of numerical values that indicate theprobability that a subject belongs to a particular psychological orpsychiatric category, or that the particular subject will likely enter aparticular psychological or psychiatric category in the near future. Thedisclosed neural stimulation system and methodology are flexible in thatother data modalities can be similarly analyzed and correlated with textfeatures and categorization to extend the analysis beyond speech.

Referring now to FIG. 9, a computer program product 900 in accordancewith an embodiment that includes a computer readable storage medium 902and program instructions 904 is generally shown.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A neural stimulation system having anon-transitory computer readable medium having program instructionsreadable by a processor circuit of the neural stimulation system forimplementing the neural stimulation system, the processor circuit of theneural stimulation system comprising: a first module configured toderive neural data from muscle contractions or movements of a subject; agraphical text analysis module communicatively coupled to the firstmodule and configured to derive neural state assessment data thatrepresents a neural state assessment of said subject based at least inpart on said neural data; and a third module communicatively coupled tothe graphical text analysis module and a fourth module; wherein thefourth module is configured to deliver electrical neural stimulationthrough an electrode to a selected location of the subject; wherein thethird module is configured to derive, based at least in part on saidneural state assessment data, at least one neural stimulation parameterof the electrical neural stimulation delivered through the electrode tothe selected location of the subject; wherein said neural data isderived from text; wherein said muscle contractions or movements producespeech; wherein said first module is further configured to convertspeech to text; wherein said graphical text analysis module is furtherconfigured to convert said neural data to a graphical set of vectors;wherein said vectors categorize a neural state of said subject.
 2. Thesystem of claim 1, wherein at least one of said first module, saidgraphical text analysis module, said third module and said fourth modulecomprises a node of a cloud.
 3. The system of claim 1, wherein: saidfirst module comprises an other inputs module and a muscle contractionsor movements module; said graphical text analysis module comprises ananalyzer module and a neural state assessment module; said third modulecomprises a neural stimulation parameter module; and said fourth modulecomprises a neural stimulator.
 4. The system of claim 1, wherein saidgraphical text analysis module is further configured to: provide a firstcategory of a cognitive state of said subject; and use said firstcategory to predict a cognitive trajectory and to update said at leastone neural stimulation parameter.
 5. The system of claim 4, wherein:said first module is further configured to derive additional neural datasamples; said additional neural data samples provide predictions ofadditional cognitive trajectories within said graphical set of vectors;and said additional neural data samples are derived from at least oneof: a written text; an electroencephalogram (EEG); a deep brainelectrophysiology; and at least one behavior of said subject.
 6. Thesystem of claim 5 wherein: said graphical text analysis module isfurther configured to calculate a probability that any of said cognitivetrajectory and said additional cognitive trajectories was produced by aparticular Markov chain.
 7. The system of claim 5, wherein: saidadditional data samples comprise said at least one neural stimulationparameter, and a second classification of cognitive state comprises adetermination that a change in said neural state of said subject wasbeneficial.
 8. The system of claim 7, wherein: said third module isfurther configured to select said at least one neural stimulationparameter based on an assessment that said at least one neuralstimulation parameter has achieved a reclassification of said neuralstate of said subject.
 9. A computer program product for implementing aneural stimulation system, the computer program product comprising: acomputer readable storage medium having program instructions embodiedtherewith, wherein the computer readable storage medium is not atransitory signal per se, the program instructions readable by aprocessor circuit to cause the processor circuit to perform a methodcomprising: deriving, by said processor, neural data from musclecontractions or movements of a subject; deriving, by said processor,neural state assessment data that represents a neural state assessmentof said subject based at least in part on said neural data; controllingan electrode to deliver electrical neural stimulation through theelectrode to a selected location of the subject; and using graphicaltext analysis to derive, by said processor, based at least in part onsaid neural state assessment data, at least one neural stimulationparameter of the electrical neural stimulation delivered through theelectrode to the selected location of the subject; wherein said neuraldata is derived from text; wherein said muscle contractions or movementsproduce speech; wherein said deriving neural data from musclecontractions or movements comprises converting speech to text; whereinsaid deriving said neural state assessment data that represents a neuralstate assessment of said subject based at least in part on said neuraldata comprises converting said neural data to a graphical set ofvectors; wherein said vectors categorize a neural state of said subject.10. A computer implemented neural stimulation system, the systemcomprising: a first module configured to derive neural data from musclecontractions or movements of a subject; a second module communicativelycoupled to the first module and configured to derive neural stateassessment data that represents a neural state assessment of saidsubject based at least in part on said neural data; and a third modulecommunicatively coupled to the second module and a fourth module;wherein the fourth module is configured to deliver electrical neuralstimulation through an electrode to a selected location of the subject;wherein the third module is configured to derive at least one neuralstimulation parameter of the electrical neural stimulation based atleast in part on said neural state assessment data; wherein said secondmodule is further configured to convert said neural data to a graphicalset of vectors; wherein said vectors categorize a neural state of saidsubject.